IDEAS home Printed from https://ideas.repec.org/a/hin/complx/6910865.html
   My bibliography  Save this article

An Improved Grey Model with Time Power and Its Application

Author

Listed:
  • Jianming Jiang
  • Caixia Liu
  • Yuanguo Yao
  • Yumu Lu
  • Wanli Xie
  • Chong Liu
  • Qingdu Li

Abstract

The grey system model with time power, which is often called the GM(1,1, tα), appeals considerable interest of research due to its effectiveness in time series forecasting. Aimed to improve further the GM(1,1, tα) model, this paper introduces a new whitening equation with variable coefficient into the original whitening equation which extends applicable scope; as a result, an improved grey model with time power, namely, OGM(1,1, tα), is proposed. Firstly, the time response function of the novel model and the restored values of original series are deduced through grey modelling techniques. Secondly, the variable coefficient in the whitening equation and the time power are determined by particle swarm optimization algorithm. Two empirical examples are then used to verify the validity of the novel model. Finally, the novel model is applied to predict the oil consumption of China from 2004 to 2018. Results show the novel model outperforms other commonly-used competitive models, which can well serve a benchmark model for scholars and decision-makers.

Suggested Citation

  • Jianming Jiang & Caixia Liu & Yuanguo Yao & Yumu Lu & Wanli Xie & Chong Liu & Qingdu Li, 2022. "An Improved Grey Model with Time Power and Its Application," Complexity, Hindawi, vol. 2022, pages 1-10, January.
  • Handle: RePEc:hin:complx:6910865
    DOI: 10.1155/2022/6910865
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/complexity/2022/6910865.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/complexity/2022/6910865.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2022/6910865?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:6910865. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.